Reduced algorithm of laser measurement data for geometric parameters of ring forgings based on Artificial Immune Algorithm

Abstract

Measuring the geometric parameters of ring forgings by laser scanner is an important way to ensure the quality of processing. However, the amount of data obtained by scanner is huge and the noise is complex. The process of data processing is slow. It is impossible to ensure the real-time quality monitoring. Therefore, an improved Quadratic Error Metric (QEM) point cloud data reduction algorithm based on Artificial Immune Algorithm (AIA) is proposed in this study. As for AIA, the choice of antigen and antibody is the critical step. The candidate point cloud is used as the antibody. The antigen is divided into two parts. The first part is the cost function. The cost function is made up of the squares sum of the volumes for two tetrahedra. This is an improved algorithm of QEM. The second part is the optimal solution of the cost function. The antibodies are screened by using the specific reactions between antigens and antibodies, antibodies and antibodies. The point cloud data can be reduced by these specific reactions. Finally, the algorithm is applied to the experiment of measuring the geometric parameters of typical ring forging. The experimental results show that the reduced data can quickly reconstruct the model. The precision of the size parameters for the model can be ensured through the algorithm proposed in the study. Through the comparison experiments, the advantages of the reduced method of the algorithm are further proved. Meanwhile, the real-time performance of the on-line monitoring of the quality for the ring forging can be improved. The measuring method proposed is feasible according to the experimental results.